Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Values and policy

Before we start talking about (PG), let’s refresh our minds with the common characteristics of the methods covered in part two of this book. The central topic in Q-learning is the value of the state or action + state pair. Value is defined as the discounted total reward that we can gather from this state or by issuing this particular action from the state. If we know the value, our decision on every step becomes simple and obvious: we just act greedily in terms of value, and that guarantees us good total reward at the end of the episode. So, the values of states (in the case of the Value Iteration method) or state + action (in the case of Q-learning) stand between us and the best reward. To obtain these values, we’ve used the Bellman equation, which expresses the value on the current step via the values on the next step.

In Chapter 1, What is Reinforcement Learning?, we defined the entity that tells us what to do in every state as policy. As in Q-learning methods, when values...